Postdoctoral In Bayesian Statistics Jobs in Virginia

Everforth ECS is seeking a Data/ML Scientist SME to work in the National Capital Region covering the Pentagon, Falls Church, and Fairfax. Please Note: This position is contingent upon contract award.
The War Data Platform (WDP) is a key initiative within the U.S. Department of War's (DoW) AI-First strategy introduced in early 2026. The WDP focuses on operational warfighting data and aims to accelerate the deployment of artificial intelligence (AI) on the battlefield. The WDP extends to Unclassified, Secret, and Top Secret environments, and supports collaboration between Combatant Commands, Joint Staff directorates, Senior Executive Service leaders, and operational analysts.
The Data/ML Scientist SME is a principal-level subject matter expert responsible for architecting and sustaining the machine learning-driven data quality capabilities that underpin the WDP Core Integration enterprise, ensuring that mission data serving Combatant Commands, Joint Staff elements, and interagency partners meets the accuracy, completeness, and timeliness standards required for AI-enabled warfighter decision advantage. This role serves as the authoritative technical voice on ML-based data quality monitoring, anomaly detection, and analytic readiness across all WDP security enclaves, and operates in close collaboration with data engineering, platform, cybersecurity, and AI integration teams to drive continuous improvement across the program's full data lifecycle.
• Architects and optimizes machine learning-driven data quality capabilities across Unclassified and NIPR, Secret and SIPR, and Top Secret and JWICS environments to advance War Data Platform (WDP) Core Integration enterprise data readiness.
• Designs, builds, and maintains data quality monitoring tools using Apache Spark, Databricks, Python validation frameworks, Great Expectations, Delta Live Tables, and cloud-native observability services to evaluate accuracy, completeness, timeliness, lineage fidelity, and schema consistency across ingest pipelines and medallion zone storage layers.
• Develops automated anomaly detection methods, statistical drift monitoring models, and ML-based pattern recognition workflows that identify deviations in mission data supporting Combatant Commands, Joint Staff elements, and interagency partners.
• Conducts analysis of alternatives on data tooling solutions, benchmarks tool performance metrics, and recommends enhancements that increase throughput, scalability, and operational reliability across all enclaves.
• Implements dashboards using Tableau, Power BI, and Databricks SQL to visualize operational data health, tool performance indicators, and mission impact assessments for senior leaders and engineering teams.
• Integrates outputs into continuous improvement cycles by collaborating with data engineering, cybersecurity, platform, and artificial intelligence teams to strengthen War Data Platform (WDP) Core Integration data governance and enterprise resilience.
• Produces technical reports, engineering findings, data quality scoring models, and modernization roadmaps that drive measurable improvements in analytic readiness, model performance, and decision superiority across the Department of War.
• Performs other duties as assigned.
• Current Secret security clearance with the ability to obtain and maintain a Top Secret (TS) security clearance with Sensitive Compartmented Information (SCI).
• 12 or more years of progressively responsible experience in data science, machine learning engineering, or a closely related field, with demonstrated expert-level proficiency designing and operationalizing ML-driven data quality and analytics capabilities in enterprise or multi-enclave defense environments.
• Experience or expertise in Bayesian statistical frameworks, including Bayesian causal inference methods for reasoning under uncertainty, evaluating intervention effects, and supporting decision-making in complex operational environments.
• Expert proficiency in Python-based data science and ML frameworks, including experience with Apache Spark, Databricks, Great Expectations, and Delta Live Tables for large-scale pipeline validation, anomaly detection, statistical drift monitoring, and medallion architecture data quality management.
• Demonstrated experience building and deploying ML models, automated validation workflows, and data observability solutions in DoW-compliant cloud environments such as AWS GovCloud or AWS Secret Region, including operations across NIPRNet, SIPRNet, and JWICS security enclaves.
• Proven ability to design and deliver executive-facing data quality dashboards and mission impact assessments using tools such as Tableau, Power BI, or Databricks SQL, and to translate complex technical findings into actionable recommendations for senior leaders and cross-functional engineering teams.
• Strong problem-solving and decision-making capabilities, with a proven ability to weigh the relative costs and benefits of potential actions and identify the most appropriate solution.
• Highly developed interpersonal and oral/written communication skills, with the ability to effectively and professionally interact with a diverse set of stakeholders (from peers to end-users to executive management).